DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
2020 ◽
Vol 34
(04)
◽
pp. 4296-4303
Keyword(s):
We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed state, DefogGAN generates defogged images of a game as predictive information. Such information can lead to create a strategic agent for the game. DefogGAN is a conditional GAN variant featuring pyramidal reconstruction loss to optimize on multiple feature resolution scales. We have validated DefogGAN empirically using a large dataset of professional StarCraft replays. Our results indicate that DefogGAN can predict the enemy buildings and combat units as accurately as professional players do and achieves a superior performance among state-of-the-art defoggers.
Keyword(s):
2018 ◽
Vol 36
(6)
◽
pp. 1052-1058
◽
Keyword(s):
2010 ◽
Vol 20
(1)
◽
pp. 9-13
◽
2021 ◽
pp. 036119812199714
Keyword(s):
Keyword(s):
2001 ◽